Tips for Starting Data Analysis

Imagine that you are handed a heap of data. How will you sort it out? How do you know what’s important and what’s not? You would probably wish that it sorts itself, as would I, but if you were to pass all that data through a data analysis software, it would mold your data according to your needs, highlighting what is essential and getting rid of what is not. 

In this article, we will be covering the various aspects of data analysis and provide you with some helpful tips to get you started with your data analysis process. 

Here is an outline of the topics we’ll discuss today: 

  • What Is Data Analysis
  • Five Tips to Start Data Analysis
  • Rules Of Analyzing Your Data
  • Several Types of Data Analysis

What is Data Analysis?

Data analysis is the process of refining your data to extract useful information that can be used later for decision-making purposes. The entire data set is first analyzed, all redundant data is removed, it is given a structure, and then all the available data is sent forward so that the collected insights can be used to make future decisions. 

For example, a company collects all the reviews it receives on a product they launched to determine how popular their product is and if they should work on making more products like that or if they should work to improve the current product. This is essential in businesses because this is how you will predict the growth of your business.

Another example of data analysis could be how you need to make a decision based on your previous actions. Here you need to examine the outcomes of your preceding steps in order to take the next step so you can avoid making the mistakes you made before. 

Data analysis is often confused with  Data Mining, but there is indeed a fine line between these two. Data mining aims to give your data some structure, whereas data mining helps you make decisions related to the information extracted from your data. Also, keep in mind that Text Analytics is separate from text analysis because text analytics focuses on the results derived from your data.

5 Important Tips for Starting Data Analysis

Here are the top five tips you need to keep in mind to start data analysis: 

  • Make sure that you are asking the right questions
  • Set your priorities straight
  • Collect all the required data
  • Analyze your data
  • Interpret the outcome of what you have analyzed 

1.     Make Sure That You Are Asking the Right Questions:

While collecting the data that you will be analyzing, you need to make sure that you are being as concise as you can so you only collect the data you need. This will save you from wasting time and putting in the extra effort. Make sure that the questions you will be asking address your needs properly to ensure you don’t miss anything.

2.     Set Your Priorities Straight:

By setting your priorities straight, we mean that you need to know the following: 

  1. What kind of information will you be collecting?
  2. How you will be collecting the information?

First of all, you need to understand the type of data you will be collecting.

For example, if you want to predict the amount of money you will be spending on your business in a year, you need to know the number of employees who work for you, their wages, and the money you spend on your physical environment office, etc. 

Second of all, you need to determine the method of collection 

For instance, in the scenario above, you are collecting the cost over a year. You need to understand what time frame would be best for you and what unit you will be measuring the total cost in.

3.     Collect all the required data:

Sourcing data can be the most crucial step because, in most cases, the data you provide will determine the outcome. For this step, you need to know what data you will be provided from your existing databases. The data you will be providing must be fully labeled and organized to ensure that no redundant data is being passed on because all of this will affect your final output.

4.     Analyze your data:

At this stage, you will be diving deep into your data because you will analyze all the data you have collected and thoroughly examine it. What you will be getting from this study will be the answer to your initial question. 

There are many methods through which you can analyze your data, e.g., by plotting your data on a graph to see how some points are related and how their relationship affects the outcome.

Some useful software that will help you in this step are Microsoft ExcelMinitab, and Stata. These software will help you perform calculations and relate your data more efficiently.

5.     Interpret the outcome of what you have analyzed:

This is the final stage of the entire process. At this point, you need to visualize your results to interpret them. While interpreting, keep in mind that you are staying true to the original purpose of your analysis. Not only that, but whatever the results, maybe you need to be able to accept them even if they do not match your expectations. Sometimes, the results may also contradict what happens in reality. 

What is the First Rule of Data Analysis?

The first and foremost rule of data analytics is to understand how two contributing factors are related to each other and how you can show them on a graph.

Each factor doesn’t necessarily need to be dependent on another. Often, one factor is wholly independent of all other factors, yet it still plays an integral part in the outcome.

That is why it is essential to understand that just because two things can be plotted together does not mean that they are entirely dependent on each other. We can only find this out by plotting all the information that we have collected; only then will we be able to understand how big of an impact every factor has. 

What You Need to Ask Yourself as You Proceed:

Before diving into the entire data analysis process, here are a few questions you can ask yourself beforehand so that you do not get confused in the middle of the whole process.

  1. Is my initial question being correctly addressed?
  2. What are the limits to the information I have found?
  3. Are the results free of the causes of my objections?

If the solution you have come up with achieves your primary purpose, you are good to go even after asking yourself these questions.

In case it has not, here are a few steps you can take to achieve your goal:

  1. Redefine your question (helps you redirect your focus to where it should be)
  2. Check the method you used to aggregate your data
  3. Clean your data and rid it of any possible errors and gaps
  4. Try out exploratory analysis (This will help you in achieving step one)
  5. Redefine your question (helps you redirect your focus to where it should be)
  6. Check the method you used to aggregate your data
  7. Clean your data and rid it of any possible errors and gaps
  8. Try out exploratory analysis (This will help you in achieving step one)

Descriptive Analysis Explained
Tips for Starting Data Analysis
Photo by PhotoMIX Company from Pexels

Here are the Different Types of Data Analysis:

Described below are the five different approaches to data analysis. You must identify the requirement of your data before selecting the method of data analysis to make sure that you do not miss anything important.

1.     Text Analysis

Text analytics is a term that is used interchangeably with data mining. This is the method of refining enormous amounts of data. It takes help from many machine learning tools that process your unstructured data and trace patterns. You then have all those insights at your disposal. Many business intelligence tools can be acquired for this purpose as well, and you can find all of these on the market! There are several types of text analytics, and you can learn more about them here.

2.     Statistical Analysis

The statistical analysis focuses on the results of your previous decisions to produce steps you can take for your next move. This model collects your data, analyzes it then, upon the interpretation of your last results, presents you with a new solution. 

This model has two types:

  1. Descriptive:

What this type does is that it inputs all your numerical data and outputs the frequency and percentages in the case of categorical data and the deviation and mean in the case of serial data.

  • Inferential:

After examining different scenarios, this type provides multiple options, i.e., alternatives from the same data set.

3.     Diagnostic Analysis

When you want to address the cause behind something or find out what kind of pattern something follows, you can use this data analytics method. When you are doing a business in which it is crucial to determine why a particular product gained more or less popularity, you can use this feature’s help to determine what happened. You can also use this when you come across an unfamiliar problem. Through this method, you can identify the cause of the problem by comparing the current situation with similar ones the company might have faced before. Finally, it will give you some practical solutions you can implement based on how you solved the problems earlier.

4.     Predictive Analysis

What this type of data analysis does is that it takes in your old data and your current data to predict the outcomes of your future decisions. The outcome mostly depends on the integrity of the data; the more accurate data you provide, the more precise results it will give. 

For example, before launching a new product, you can first cross-check how popular the products you launched previously were and how well your company is doing right now. The outcome will give you an estimate of how popular the new product will end up being.

Another example could be weather forecasting. Over some time, the weather and climate condition of an area is examined, and all other factors that may affect the weather are kept in mind, after which the future weather condition predictions are presented. 

5.     Prescriptive Analysis

Prescriptive data analysis works like a combination of descriptive and predictive data analysis because it takes in all the data to give out the most viable solution. It takes in and analyzes the data of all current and former problems, studies them, and then finally concludes by providing you with the most effective solution. This way, you can quickly resolve complex problems. This method saves you from the hassle of choosing your action plan and saves your time as well. That is why many enterprises use this method. 

To find out more about the diverse ways through which you can implement these methods, click here.

Wrap Up

Data analysis plays a vital role in the progress of your business. Thanks to data analysis, many major companies today find it easier to keep up with the increasing number of customers’ demands. It collects all the reviews (even if they are large in number), examines them, summarizes them, provides us with valuable insights, and saves our time and money. This then allows those businesses to compete in the now intensely competitive market. 

Many different examples and methods of performing data analysis have been discussed in the article above. By now, you must be familiar with the steps you can take to perform data analysis effectively if you are a beginner

If you are interested in more related information, check out this site.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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